Hidden Markov Modeling of eye movements with image information leads to better discovery of regions of interest

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
PublisherThe Cognitive Science Society
Pages1032-1037
ISBN (print)9780991196739
Publication statusPublished - 2016

Publication series

NameProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016

Conference

Title38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
PlaceUnited States
CityPhiladelphia
Period10 - 13 August 2016

Abstract

Hidden Markov models (HMM) can describe the spatial and temporal characteristics of eye-tracking recordings in cognitive tasks. Here, we introduce a new HMM approach. We developed HMMs based on fixation locations and we also used image information as an input feature. We demonstrate the benefits of the newly proposed model in a face recognition study wherein an HMM was developed for every subject. Discovery of regions of interest on facial stimuli is improved as compared with earlier approaches. Moreover, clustering of the newly developed HMMs lead to very distinct groups. The newly developed approach also allows reconstructing image information at each fixation. © Copyright 2022 Elsevier B.V., All rights reserved.

Research Area(s)

  • Eye-tracking, Face Recognition, Hidden Markov Model, Machine Learning

Citation Format(s)

Hidden Markov Modeling of eye movements with image information leads to better discovery of regions of interest. / Brueggemann, Stephan; Chan, Antoni B.; Hsiao, Janet H.
Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. The Cognitive Science Society, 2016. p. 1032-1037 (Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review